bioRxivBiophysicsPreprint
Biophysical Basis of the in vivo Electroretinogram of the Mouse: 4-Shell Current Source Density Model
Gabriel PeinadoiD, Ratheesh KumariD, Vincent YaoiD, David John CalkinsiD, Paul FitzGerald, William E. SchiesseriD, Edward N. PughiD
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Abstract
A goal of contemporary physiology is to translate the knowledge obtained ex vivo of the molecular structure and function of ionic mechanisms into tools for quantitative, in vivo measurement of that function in humans, and in animal models of disease and therapeutic intervention. Non-invasive field potentials such as ECGs, EMGs, EEGs, and ERGs hold great promise in efforts to achieve this goal, but their full translation is challenging due to the multiplicity of cells with distinct ionic mechanisms and distributions of membrane current sources and sinks, and the requirement of adequate characterization of volume conduction in the relevant tissue(s). The molecular identities and subcellular distributions of the ionic mechanisms of mouse rod photoreceptors and the adjacent retinal pigment epithelium (RPE) have been thoroughly characterized, and are associated with two major components of the ERG, the a-wave and the c-wave, respectively. To develop a molecular-biophysical description of these components of the ERG, we have pharmacologically and genetically isolated rod photoreceptor-driven currents, created a 3D "4-shell" model of volume conduction in the mouse eye and extraocular tissue, and solved the Equation of Continuity for trans-photoreceptor layer and trans-RPE layer sources. Corneal and intraocular measurements of a- and c-waves are shown to reject the classic Rodieck-Ford electrical circuit model of the ERG, but found consistent with a 4-shell model having realistic values for extracellular conductivity in the eye and extraocular tissues. Our results and analysis explain the large variation across studies in maximal a-wave amplitudes and show how a-wave amplitudes exceeding 1 mV can be achieved.
- organism: mouse
- assay: electrophysiology
- research focus: synaptic & neural circuits
- approach: biophysical modeling
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